Integrative network analysis suggests prioritised drugs for atopic dermatitis.

BACKGROUND: Atopic dermatitis (AD) is a prevalent chronic inflammatory skin disease whose pathophysiology involves the interplay between genetic and environmental factors, ultimately leading to dysfunction of the epidermis. While several treatments are effective in symptom management, many existing therapies offer only temporary relief and often come with side effects. For this reason, the formulation of an effective therapeutic plan is challenging and there is a need for more effective and targeted treatments that address the root causes of the condition. Here, we hypothesise that modelling the complexity of the molecular buildup of the atopic dermatitis can be a concrete means to drive drug discovery. METHODS: We preprocessed, harmonised and integrated publicly available transcriptomics datasets of lesional and non-lesional skin from AD patients. We inferred co-expression network models of both AD lesional and non-lesional skin and exploited their interactional properties by integrating them with a priori knowledge in order to extrapolate a robust AD disease module. Pharmacophore-based virtual screening was then utilised to build a tailored library of compounds potentially active for AD. RESULTS: In this study, we identified a core disease module for AD, pinpointing known and unknown molecular determinants underlying the skin lesions. We identified skin- and immune-cell type signatures expressed by the disease module, and characterised the impaired cellular functions underlying the complex phenotype of atopic dermatitis. Therefore, by investigating the connectivity of genes belonging to the AD module, we prioritised novel putative biomarkers of the disease. Finally, we defined a tailored compound library by characterising the therapeutic potential of drugs targeting genes within the disease module to facilitate and tailor future drug discovery efforts towards novel pharmacological strategies for AD. CONCLUSIONS: Overall, our study reveals a core disease module providing unprecedented information about genetic, transcriptional and pharmacological relationships that foster drug discovery in atopic dermatitis.

Analysis of phenotypic changes in high temperature and low pH extreme conditions of Alicyclobacillus sendaiensis PA2 related with the cell wall and sporulation genes.

The A. sendaiensis PA2 is a polyextremophile bacterium. In this study, we analyze the A. sendaiensis PA2 genome. The genome was assembled and annotated. The A. sendaiensis PA2 genome structure consists of a 2,956,928 bp long chromosome and 62.77% of G + C content. 3056 CDSs were predicted, and 2921 genes were assigned to a putative function. The ANIm and ANIb value resulted in 97.17% and 96.65%, the DDH value was 75.5%, and the value of TETRA (Z-score) was 0.98. Comparative genomic analyses indicated that three systems are enriched in A. sendaiensis PA2. This strain has phenotypic changes in cell wall during batch culture at 65 °C, pH 5.0 and without carbon and nitrogen source. The presence of unique genes of cell wall and sporulation subsystem could be related to the adaptation of A. sendaiensis PA2 to hostile conditions.

Impact of specialist ataxia centres on health service resource utilisation and costs across Europe: cross-sectional survey.

BACKGROUND: Little is known about the costs of treating ataxia and whether treatment at a specialist ataxia centre affects the cost of care. The aim of this study was to investigate whether patients who attended specialist ataxia centres in three European countries reported differences in their health care use and costs compared with patients who did not attend a specialist ataxia centre. We compared mean resource use and health service costs per patient affected by ataxia in the United Kingdom, Italy and Germany over a 12-month period. Data were obtained from a survey distributed to people with ataxia in the three countries. We compared mean resource use for each contact type and costs, stratifying patients by whether they were currently attending a specialist ataxia centre or had never attended one. RESULTS: Responses were received from 181 patients from the United Kingdom, 96 from Italy and 43 from Germany. Differences in the numbers of contacts for most types of health service use between the specialist ataxia centre and non-specialist ataxia centre groups were non-significant. In the United Kingdom the mean total cost per patient was €2209 for non-specialist ataxia centre patients and €1813 for specialist ataxia centre patients (P = 0.59). In Italy these figures were €2126 and €1971, respectively (P = 0.84). In Germany they were €2431 and €4087, respectively (P = 0.19). Inpatient stays made the largest contribution to total costs. CONCLUSIONS: Within each country, resource use and costs were broadly similar for specialist ataxia centre and non-specialist ataxia centre groups. There were differences between countries in terms of health care contacts and costs.

Patient pathways for rare diseases in Europe: ataxia as an example.

BACKGROUND: Progressive ataxias are rare and complex neurological disorders that represent a challenge for the clinicians to diagnose and manage them. This study explored the patient pathways of individuals attending specialist ataxia centres (SAC) compared with non-specialist settings. We investigated specifically how diagnosis was reached, the access to healthcare services, treatments, and care satisfaction. The focus of this study was on early intervention, coordination of treatment to understand the care provision in different countries. METHODS: A patient survey was done in the UK, Germany and Italy to gather information about diagnosis and management of the ataxias in specialist (SAC) and non-specialist settings, utilisation of other primary and secondary health care services, and patients' satisfaction of received treatment. RESULTS: Patients gave positive feedback about the role of SAC in understanding their condition, ways to manage their ataxia (p < 0.001; UK) and delivering care adapted to their needs (p < 0.001; UK), in coordinating referrals to other healthcare specialists, and in offering opportunities to take part in research studies. Similar barriers for patients were identified in accessing the SACs among the selected countries, UK, Germany, and Italy. CONCLUSIONS: This study provides crucial information about the ataxia patients care pathways in three European countries. Overall, the results showed a trend in patients' satisfaction being better in SAC compared to non-SAC. The outcomes can be used now for policy recommendations on how to improve treatment and care for people with these very rare and complex neurological diseases across Europe.

DREAM: an R package for druggability evaluation of human complex diseases.

MOTIVATION: De novo drug development is a long and expensive process that poses significant challenges from the design to the preclinical testing, making the introduction into the market slow and difficult. This limitation paved the way to the development of drug repurposing, which consists in the re-usage of already approved drugs, developed for other therapeutic indications. Although several efforts have been carried out in the last decade in order to achieve clinically relevant drug repurposing predictions, the amount of repurposed drugs that have been employed in actual pharmacological therapies is still limited. On one hand, mechanistic approaches, including profile-based and network-based methods, exploit the wealth of data about drug sensitivity and perturbational profiles as well as disease transcriptomics profiles. On the other hand, chemocentric approaches, including structure-based methods, take into consideration the intrinsic structural properties of the drugs and their molecular targets. The poor integration between mechanistic and chemocentric approaches is one of the main limiting factors behind the poor translatability of drug repurposing predictions into the clinics. RESULTS: In this work, we introduce DREAM, an R package aimed to integrate mechanistic and chemocentric approaches in a unified computational workflow. DREAM is devoted to the druggability evaluation of pathological conditions of interest, leveraging robust drug repurposing predictions. In addition, the user can derive optimized sets of drugs putatively suitable for combination therapy. In order to show the functionalities of the DREAM package, we report a case study on atopic dermatitis. AVAILABILITY AND IMPLEMENTATION: DREAM is freely available at https://github.com/fhaive/dream. The docker image of DREAM is available at: https://hub.docker.com/r/fhaive/dream.

ESPERANTO: a GLP-field sEmi-SuPERvised toxicogenomics metadAta curatioN TOol.

SUMMARY: Biological data repositories are an invaluable source of publicly available research evidence. Unfortunately, the lack of convergence of the scientific community on a common metadata annotation strategy has resulted in large amounts of data with low FAIRness (Findable, Accessible, Interoperable and Reusable). The possibility of generating high-quality insights from their integration relies on data curation, which is typically an error-prone process while also being expensive in terms of time and human labour. Here, we present ESPERANTO, an innovative framework that enables a standardized semi-supervised harmonization and integration of toxicogenomics metadata and increases their FAIRness in a Good Laboratory Practice-compliant fashion. The harmonization across metadata is guaranteed with the definition of an ad hoc vocabulary. The tool interface is designed to support the user in metadata harmonization in a user-friendly manner, regardless of the background and the type of expertise. AVAILABILITY AND IMPLEMENTATION: ESPERANTO and its user manual are freely available for academic purposes at https://github.com/fhaive/esperanto. The input and the results showcased in Supplementary File S1 are available at the same link.

Targeting metabolism by B-raf inhibitors and diclofenac restrains the viability of BRAF-mutated thyroid carcinomas with Hif-1α-mediated glycolytic phenotype.

BACKGROUND: B-raf inhibitors (BRAFi) are effective for BRAF-mutated papillary (PTC) and anaplastic (ATC) thyroid carcinomas, although acquired resistance impairs tumour cells' sensitivity and/or limits drug efficacy. Targeting metabolic vulnerabilities is emerging as powerful approach in cancer. METHODS: In silico analyses identified metabolic gene signatures and Hif-1α as glycolysis regulator in PTC. BRAF-mutated PTC, ATC and control thyroid cell lines were exposed to HIF1A siRNAs or chemical/drug treatments (CoCl2, EGF, HGF, BRAFi, MEKi and diclofenac). Genes/proteins expression, glucose uptake, lactate quantification and viability assays were used to investigate the metabolic vulnerability of BRAF-mutated cells. RESULTS: A specific metabolic gene signature was identified as a hallmark of BRAF-mutated tumours, which display a glycolytic phenotype, characterised by enhanced glucose uptake, lactate efflux and increased expression of Hif-1α-modulated glycolytic genes. Indeed, Hif-1α stabilisation counteracts the inhibitory effects of BRAFi on these genes and on cell viability. Interestingly, targeting metabolic routes with BRAFi and diclofenac combination we could restrain the glycolytic phenotype and synergistically reduce tumour cells' viability. CONCLUSION: The identification of a metabolic vulnerability of BRAF-mutated carcinomas and the capacity BRAFi and diclofenac combination to target metabolism open new therapeutic perspectives in maximising drug efficacy and reducing the onset of secondary resistance and drug-related toxicity.

KNeMAP: a network mapping approach for knowledge-driven comparison of transcriptomic profiles.

MOTIVATION: Transcriptomic data can be used to describe the mechanism of action (MOA) of a chemical compound. However, omics data tend to be complex and prone to noise, making the comparison of different datasets challenging. Often, transcriptomic profiles are compared at the level of individual gene expression values, or sets of differentially expressed genes. Such approaches can suffer from underlying technical and biological variance, such as the biological system exposed on or the machine/method used to measure gene expression data, technical errors and further neglect the relationships between the genes. We propose a network mapping approach for knowledge-driven comparison of transcriptomic profiles (KNeMAP), which combines genes into similarity groups based on multiple levels of prior information, hence adding a higher-level view onto the individual gene view. When comparing KNeMAP with fold change (expression) based and deregulated gene set-based methods, KNeMAP was able to group compounds with higher accuracy with respect to prior information as well as is less prone to noise corrupted data. RESULT: We applied KNeMAP to analyze the Connectivity Map dataset, where the gene expression changes of three cell lines were analyzed after treatment with 676 drugs as well as the Fortino et al. dataset where two cell lines with 31 nanomaterials were analyzed. Although the expression profiles across the biological systems are highly different, KNeMAP was able to identify sets of compounds that induce similar molecular responses when exposed on the same biological system. AVAILABILITY AND IMPLEMENTATION: Relevant data and the KNeMAP function is available at: https://github.com/fhaive/KNeMAP and 10.5281/zenodo.7334711.

OxLDL sensitizes platelets for increased formation of extracellular vesicles capable of finetuning macrophage gene expression.

Platelet extracellular vesicles (PEVs) generated upon platelet activation may play a role in inflammatory pathologies such as atherosclerosis. Oxidized low-density lipoprotein (oxLDL), a well-known contributor to atherogenesis, activates platelets and presensitizes them for activation by other agonists. We studied the effect of oxLDL on the secretion, composition, and inflammatory functions of PEVs using contemporary EV analytics. Platelets were activated by co-stimulation with thrombin (T) and collagen (C) ± oxLDL and characterized by high-resolution flow cytometry, nanoparticle tracking analysis, proximity extension assay, western blot, and electron microscopy. The effect of PEVs on macrophage differentiation and functionality was examined by analyzing macrophage surface markers, cytokine secretion, and transcriptome. OxLDL upregulated TC-induced formation of CD61+, P-selectin+ and phosphatidylserine+ PEVs. Blocking the scavenger receptor CD36 significantly suppressed the oxLDL+TC-induced PEV formation, and HDL caused a slight but detectable suppression. The inflammatory protein cargo differed between the PEVs from stimulated and unstimulated platelets. Both oxLDL+TC- and TC-induced PEVs enhanced macrophage HLA-DR and CD86 expression and decreased CD11c expression as well as secretion of several cytokines. Pathways related to cell cycle and regulation of gene expression, and immune system signaling were overrepresented in the differentially expressed genes between TC PEV -treated vs. control macrophages and oxLDL+TC PEV -treated vs. control macrophages, respectively. In conclusion, we speculate that oxLDL and activated platelets contribute to proatherogenic processes by increasing the number of PEVs that provide an adhesive and procoagulant surface, contain inflammatory mediators, and subtly finetune the macrophage gene expression.

Toxicogenomics Data for Chemical Safety Assessment and Development of New Approach Methodologies: An Adverse Outcome Pathway-Based Approach.

Mechanistic toxicology provides a powerful approach to inform on the safety of chemicals and the development of safe-by-design compounds. Although toxicogenomics supports mechanistic evaluation of chemical exposures, its implementation into the regulatory framework is hindered by uncertainties in the analysis and interpretation of such data. The use of mechanistic evidence through the adverse outcome pathway (AOP) concept is promoted for the development of new approach methodologies (NAMs) that can reduce animal experimentation. However, to unleash the full potential of AOPs and build confidence into toxicogenomics, robust associations between AOPs and patterns of molecular alteration need to be established. Systematic curation of molecular events to AOPs will create the much-needed link between toxicogenomics and systemic mechanisms depicted by the AOPs. This, in turn, will introduce novel ways of benefitting from the AOPs, including predictive models and targeted assays, while also reducing the need for multiple testing strategies. Hence, a multi-step strategy to annotate AOPs is developed, and the resulting associations are applied to successfully highlight relevant adverse outcomes for chemical exposures with strong in vitro and in vivo convergence, supporting chemical grouping and other data-driven approaches. Finally, a panel of AOP-derived in vitro biomarkers for pulmonary fibrosis (PF) is identified and experimentally validated.

Effects of a 12-week supervised resistance training program, combined with home-based physical activity, on physical fitness and quality of life in female breast cancer survivors: the EFICAN randomized controlled trial.

PURPOSE: This study assessed the effects of 12-week supervised resistance training combined with home-based physical activity on physical fitness, cancer-related fatigue, depressive symptoms, health-related quality of life (HRQoL), and life satisfaction in female breast cancer survivors. METHODS: A parallel-group, outcome assessor-blinded, randomized controlled trial included 60 female breast cancer survivors who had completed their core treatments within the previous 10 years. Through computer-generated simple randomization, participants were assigned to resistance training (RTG; two sessions/week for 12 weeks plus instructions to undertake ≥ 10,000 steps/d) or control (CG; ≥ 10,000 steps/d only). Outcomes were evaluated at baseline and week 12. Muscular strength was assessed with electromechanical dynamometry. A standardized full-body muscular strength score was the primary outcome. Secondary outcomes included cardiorespiratory fitness, shoulder mobility, cancer-related fatigue, depressive symptoms, HRQoL, and life satisfaction. RESULTS: Thirty-two participants were assigned to RTG (29 achieved ≥ 75% attendance) and 28 to CG (all completed the trial). Intention-to-treat analyses revealed that the standardized full-body muscular strength score increased significantly in the RTG compared to the CG (0.718; 95% CI 0.361-1.074, P < 0.001, Cohen's d = 1.04). This increase was consistent for the standardized scores of upper-body (0.727; 95% CI 0.294-1.160, P = 0.001, d = 0.87) and lower-body (0.709; 95% CI 0.324-1.094, P = 0.001, d = 0.96) strength. There was no effect on cardiorespiratory fitness, shoulder flexion, cancer-related fatigue, depressive symptoms, HRQoL, or life satisfaction. The sensitivity analyses confirmed these results. CONCLUSION: and implication for cancer survivors. In female breast cancer survivors who had completed their core treatments within the past 10 years, adding two weekly sessions of supervised resistance training to a prescription of home-based physical activity for 12 weeks produced a large increase in upper-, lower-, and full-body muscular strength, while other fitness components and patient-reported outcomes did not improve. TRIAL REGISTRATION NUMBER: ISRCTN14601208.

The integration of large-scale public data and network analysis uncovers molecular characteristics of psoriasis.

In recent years, a growing interest in the characterization of the molecular basis of psoriasis has been observed. However, despite the availability of a large amount of molecular data, many pathogenic mechanisms of psoriasis are still poorly understood. In this study, we performed an integrated analysis of 23 public transcriptomic datasets encompassing both lesional and uninvolved skin samples from psoriasis patients. We defined comprehensive gene co-expression network models of psoriatic lesions and uninvolved skin. Moreover, we curated and exploited a wide range of functional information from multiple public sources in order to systematically annotate the inferred networks. The integrated analysis of transcriptomics data and co-expression networks highlighted genes that are frequently dysregulated and show aberrant patterns of connectivity in the psoriatic lesion compared with the unaffected skin. Our approach allowed us to also identify plausible, previously unknown, actors in the expression of the psoriasis phenotype. Finally, we characterized communities of co-expressed genes associated with relevant molecular functions and expression signatures of specific immune cell types associated with the psoriasis lesion. Overall, integrating experimental driven results with curated functional information from public repositories represents an efficient approach to empower knowledge generation about psoriasis and may be applicable to other complex diseases.

Volatile Compounds in Tofu Obtained by Soy Milk Fermentation with Lactobacillus plantarum BAL-03-ITTG and Lactobacillus fermentum BAL-21-ITTG.

Tofu is one of the main foods made with soybeans. The aim of this work was to evaluate the effect of L. plantarum and L. fermentum on the volatile compounds and sensorial profile of fermented tofu during ripening. The soy milk was fermented separately with two native strains (L. plantarum or L. fermentum) until reaching a pH of 5.5, and the fermented tofu was obtained. The tofu obtained by acidification with lactic acid was used as a control and was characterized by microbial survival (L. plantarum, L. fermentum, and P. freudenreichii) for 0, 20, and 40 days of storage at 15 °C. Moreover, the lactic and acetic acid content was determined by high-performance liquid chromatography (HPLC), and the volatile compounds were evaluated by gas. Chromatography-mass spectrometry (GC-MS). The results were analyzed by an ANOVA test (P < 0.05). After storage, the lactic acid bacteria (LAB) survived in the fermented tofu at a concentration higher than 8.0 log CFU/g after 40 days of storage. The shelf life of fermented tofu obtained by acidification was fewer than 20 days because of the presence of fungi and yeasts. The hexanal content was reduced by approximately 96% (P < 0.05) in the tofu obtained by fermentation compared with the control. This process for fermented tofu production employing two native strains could be used for industrial purposes.

The potential of a data centred approach & knowledge graph data representation in chemical safety and drug design.

Big Data pervades nearly all areas of life sciences, yet the analysis of large integrated data sets remains a major challenge. Moreover, the field of life sciences is highly fragmented and, consequently, so is its data, knowledge, and standards. This, in turn, makes integrated data analysis and knowledge gathering across sub-fields a demanding task. At the same time, the integration of various research angles and data types is crucial for modelling the complexity of organisms and biological processes in a holistic manner. This is especially valid in the context of drug development and chemical safety assessment where computational methods can provide solutions for the urgent need of fast, effective, and sustainable approaches. At the same time, such computational methods require the development of methodologies suitable for an integrated and data centred Big Data view. Here we discuss Knowledge Graphs (KG) as a solution to a data centred analysis approach for drug and chemical development and safety assessment. KGs are knowledge bases, data analysis engines, and knowledge discovery systems all in one, allowing them to be used from simple data retrieval, over meta-analysis to complex predictive and knowledge discovery systems. Therefore, KGs have immense potential to advance the data centred approach, the re-usability, and informativity of data. Furthermore, they can improve the power of analysis, and the complexity of modelled processes, all while providing knowledge in a natively human understandable network data model.

Asexually propagated Agave tequilana var. azul exhibits variation in genetic markers and defence responses to Fusarium solani.

Agave (Agave tequilana var. azul) is considered a crop with low genetic diversity because it has been propagated vegetatively for centuries for commercial purposes, and consequently, it could be equally susceptible to pests and diseases. However, the present study employs plant material derived from field-grown plants exhibiting phenotypic variability in susceptibility to agave wilt. The offshoots from rhizomes of these plants were reproduced in vitro and classified as potentially resistant or susceptible. Amplified fragment length polymorphism analysis confirmed wide genetic differences among individuals, but these differences were not correlated with the observed phenotypic variability in resistance. Propagated plantlets were inoculated with Fusarium solani in two time-lapse confrontations for 72 h and 30 days. The early biochemical response showed statistically superior levels in the accumulation of shikimic acid, phenolic compounds, and chitinase activity in potentially resistant plantlets. There was an inverse correlation of these early biochemical responses and salicylic acid and the incidence of diseased root cells in isogenic plantlets in the 30-day confrontation with F. solani, suggesting that these activities and accumulation of molecules were primordial in the defence against this pathogen.

Integrated Network Pharmacology Approach for Drug Combination Discovery: A Multi-Cancer Case Study.

Despite remarkable efforts of computational and predictive pharmacology to improve therapeutic strategies for complex diseases, only in a few cases have the predictions been eventually employed in the clinics. One of the reasons behind this drawback is that current predictive approaches are based only on the integration of molecular perturbation of a certain disease with drug sensitivity signatures, neglecting intrinsic properties of the drugs. Here we integrate mechanistic and chemocentric approaches to drug repositioning by developing an innovative network pharmacology strategy. We developed a multilayer network-based computational framework integrating perturbational signatures of the disease as well as intrinsic characteristics of the drugs, such as their mechanism of action and chemical structure. We present five case studies carried out on public data from The Cancer Genome Atlas, including invasive breast cancer, colon adenocarcinoma, lung squamous cell carcinoma, hepatocellular carcinoma and prostate adenocarcinoma. Our results highlight paclitaxel as a suitable drug for combination therapy for many of the considered cancer types. In addition, several non-cancer-related genes representing unusual drug targets were identified as potential candidates for pharmacological treatment of cancer.

Nextcast: A software suite to analyse and model toxicogenomics data.

The recent advancements in toxicogenomics have led to the availability of large omics data sets, representing the starting point for studying the exposure mechanism of action and identifying candidate biomarkers for toxicity prediction. The current lack of standard methods in data generation and analysis hampers the full exploitation of toxicogenomics-based evidence in regulatory risk assessment. Moreover, the pipelines for the preprocessing and downstream analyses of toxicogenomic data sets can be quite challenging to implement. During the years, we have developed a number of software packages to address specific questions related to multiple steps of toxicogenomics data analysis and modelling. In this review we present the Nextcast software collection and discuss how its individual tools can be combined into efficient pipelines to answer specific biological questions. Nextcast components are of great support to the scientific community for analysing and interpreting large data sets for the toxicity evaluation of compounds in an unbiased, straightforward, and reliable manner. The Nextcast software suite is available at: ( https://github.com/fhaive/nextcast).

European Academy of Neurology guidance for developing and reporting clinical practice guidelines on rare neurological diseases.

BACKGROUND AND PURPOSE: Rare diseases affect up to 29 million people in the European Union, and almost 50% of them affect the nervous system or muscles. Delays in diagnosis and treatment onset and insufficient treatment choices are common. Clinical practice guidelines (CPGs) may improve the diagnosis and treatment of patients and optimize care pathways, delivering the best scientific evidence to all clinicians treating these patients. Recommendations are set for developing and reporting high-quality CPGs on rare neurological diseases (RNDs) within the European Academy of Neurology (EAN), through a consensus procedure. METHODS: A group of 27 experts generated an initial list of items that were evaluated through a two-step Delphi consensus procedure and a face-to-face meeting. The final list of items was reviewed by an external review group of 58 members. RESULTS: The consensus procedure yielded 63 final items. Items are listed according to the domains of the AGREE instruments and concern scope and purpose, stakeholder involvement, rigour of development, and applicability. Additional items consider reporting and ethical issues. Recommendations are supported by practical examples derived from published guidelines and are presented in two tables: (1) items specific to RND CPGs, and general guideline items of special importance for RNDs, or often neglected; (2) items for guideline development within the EAN. CONCLUSIONS: This guidance aims to provide solutions to the issues specific to RNDs. This consensus document, produced by many experts in various fields, is considered to serve as a starting point for further harmonization and for increasing the quality of CPGs in the field of RNDs.

Neurological music therapy during the COVID-19 outbreak: updates and future challenges.

BACKGROUND: In the last few years, several studies confirmed the effectiveness of music therapy (MT) for the rehabilitative management of patients with neurological disorders. AIM: Here we discuss the feasibility and disadvantages of tele-neurological MT (tele-NMT) compared to the traditional MT programmes. METHODS: We selected all the articles registered in the Web of Knowledge, PubMed, Google Scholar and ScienceDirect from March 2020 to November 2021 concerning tele-NMT during the COVID-19 outbreak, collecting same examples and experiences. RESULTS: With the advent of the COVID-19, several music-based interventions (MBIs) have been adapted from "in person" to a "remote and virtual" mode (through the telemedicine). DISCUSSION: Tele-NMT could represent a promising option to provide constant care and support to people with neurological diseases during the pandemic.